Cancer (malignant tumor or malignant neoplasm), is a group of diseases involving abnormal cell growth with the potential to invade or spread to other parts of the body. Cancer are extremely diverse and various underlying molecular mechanism are involved therewith. Accordingly, clinical therapeutic protocol and prognosis of patients diagnosed with various cancers, may be drastically different depending on accurate diagnosis of underlying molecular mechanism as well as identification of the oncogenic mutations and auto and paracrine effects. In cancer patients, many of the signaling pathways that are involved in control of cell growth and differentiation are regulated in an abnormal fashion, as a result of mutations in key proteins in these pathways.
The complexity and heterogeneity of cancer demands a more sensitive and discerning identification approach that can simulate the tumor signaling pathway and identify patient specific driver mutations. For example, international publication no. WO 2014/111936 to inventors of the current application is directed to methods and systems for identifying patient specific driver mutations. The current state of the art is that only few individual markers can be used to predict treatment efficacy and toxicity. Moreover, the suitability of whole-genome sequencing (next generation sequencing) for selection of targeted therapy is limited due to the large pool of mutations accumulating within the tumor, the limited repertoire of identified driver mutations, and the very limited insight as to the interplay of the various mutations an, in particular, activity thereof.
Thus, there is unmet need in the art for methods and systems that allow identification of specific drug response of patient-specific deriver mutation(s) for determining personalized and optimized drug treatment, which is more efficient, safer and is both cost and time effective, and which has the ability to specifically be adjusted and optimized to the patient specific driver mutations.